According to recent Nielsen numbers on the topic of Internet ad-spend versus TV ad-spend, the Internet is like a small bird picking mites off the back of an elephant. I will admit these numbers are nine months old, but I do believe they still pertain, even as mobile ad-spend has pushed Internet spend higher in the last couple of years.

Nielsen gives less than 6 percent to “on line” while “television” (which includes cable) gets close to 60 percent of total global ad spend (including all advertising everywhere). If you think broadcast is still relevant, it might interest you to know that the Internet has already surpassed it. And while TV has grown much slower than the Internet, the Internet is still very small.

While Nielsen does not publish dollar amounts, ZenithOptimedia says the global total ad-spend surpassed half a trillion dollars in 2013. So both the little bird and the big elephant are billion-dollar babies. Zenith also gives the Internet a whopping 21 percent of spend, still dwarfed by TV. This may be because Nielsen gives no credit to search-engine ads, only display ads. Which is only a little bit silly, because last I read, Google has more ad-spend than all of print media in the U.S.

The elephant and the little bird are both “in the room,” if you will.

What’s the Hold-Up?

Why is TV still dominant? I read somewhere we had stopped watching TV; so I must have missed something.

In light of all the above, why is Internet the darling and TV too often portrayed as somehow the inevitable loser in this race?

If you want to hear answers from the tech community, it’s a mere statistical anomaly that the Internet has not yet completed its domination; and that advertisers just need to get with it. If you want to hear answers from advertisers, especially those that control the massive TV ad budgets, they may tell you it’s because when they go to buy ad space on the Internet, they don’t know what in the heck they are buying.

TV ad spend is pretty much ruled by Nielsen and that is because it always has been that way, and it is an article of faith that Nielsen knows best.

However silly that might be, the fact there is a single source of well-defined audience measurement for TV makes all the difference.

Digital analytics, which is supposed to be much more accurate than the extrapolated data from Nielsen, cannot begin to compete; not least because it is like a mirror shattered into a million pieces — each shard with its own reflection — while Nielsen is just a single mirror where you can see stuff.

So, is it really about slavish fealty to Nielsen? Or is there some other reason why Internet advertising continues to lag despite robust year-over-year growth?

Maybe It’s the Content

Is it possible the answer is that no one can stand Internet ads? And that they often refuse to click on them? I have one friend who says that whatever brand shows up in his free mail account is the brand he will not buy. He never said that about the ads that interrupted the ballgame.

I think there are natural limits to every market, and that Internet advertising may soon find that limit. It isn’t because people don’t love the Internet. It’s because when they watch TV, some commercials actually get their attention and make them laugh or listen. I have some doubt as to whether anyone anywhere has ever said to themselves, “Now that’s a cool Internet ad.”

I don’t have the answer — and I don’t click on ads either. But I do like that ad on TV with the hamsters driving a little Korean car.

In fact, if your digital footprint is large and complex, you ought to be clamoring for it.

That such an arcane digital offering should in fact be a bedrock of data collection success is only testament to its primacy.

Why should you care?

Let me first state the problem:

In a galaxy far, far away, perhaps they continue to use server log files to figure out what users did on a website, but in that same galaxy they are probably still experimenting with more effective ways to build a fire with two sticks and no matches. On Earth, almost no one uses log files anymore.

We deploy digital analytics tools that rely on a much more targeted and accurate manner of collecting user data: tagging.

Why is this such a big deal, and why is improper or nonexistent tagging the most common stumbling block to reliable analytics?

It’s a big deal because tags enable (relatively) precise data collection. It’s also a big deal because getting tags properly implemented and then managed in an enterprise is about as easy as herding jungle cats – the kind that maul you suddenly.

A marketer says they need to know X, Y, and Z about user behavior on a digital property. Typically it goes beyond the basics of unique visitors and total page views. In order to get reports that illuminate user behavior, the marketer needs someone to create a “tag specification”: a document, often a spreadsheet, that describes the reporting need and the tag (small snippets of code) that must be placed in HTML so that when reports are created, they in fact have data in them. This part is usually executed without too much difficulty as long as the tag-specifier knows the tools and the tags very well.

Then, too often in my experience, the chaos really begins.

That’s because people who create tag specifications do not control (and do not want to control) the HTML that drives the site. The tagging spec needs to be handed off to developers (not the marketers) so they can place these tags. Sometimes it goes OK. Often it does not. Tags are placed, but incorrectly. Or they are not placed, and with little explanation as to why they are not. Weeks go by. Marketers get frustrated and too often end up settling for much more basic reporting than they had hoped. Often they end up with whatever basic reporting comes from putting just the simple tag provided by the vendor and the tagging spec becomes an artifact of hope but with little chance of becoming a robust reporting suite.

Compounding the problem in an enterprise is the multitude of sites that have wildcatted their own analytics, usually flawed implementations of free tools like Google Analytics (which can be quite powerful when properly deployed). Nothing can be measured against anything else because there is no common tagging protocol, no agreement on what is measured and how it is reported upon.

Tag management solves both of these problems rather handily and should be part of every marketer’s toolkit where they have more than a couple of sites to manage.

There are several flavors of tag management tools, ranging from Tealium’s container paradigm to Ensighten’s “server conversation” to Adobe’s integrated package and more. What they all have in common is inherent in the category name: They manage tags and tagging.

With tag management, instead of sending off a tagging spec to a range of developers responsible for different sites hoping they all do the tagging in the same way, the marketer can have one group of developers implement a tagging structure once, and then deploy it widely.

Moreover, they can, using a graphical user interface (anyone remember the term “GUI”?), decide the conditions under which certain tags “fire” (become active); and on which digital assets. Parameters can be set globally and then with little fanfare, the tag management system actually manages these tags globally. With robust error-detection and selective deployment capabilities, many if not most of the problems associated with data collection simply go away.

Tag management tools centralize the management of data collection and can be the foundation of excellence in reporting. It’s really that simple.

You ought not be bored by tag management. You ought to be excited about it, you ought to deploy it, you have every right to benefit from it once deployed. There is little excuse today for an enterprise not to use tag management in maintaining control over data collection and reporting over a wide range of sites.

If you’re still wondering why your enterprise analytics are in disarray, you now know why. Don’t let a good tag specification go down to defeat. Collect data reliably and globally.

Digital analytics today is burdened by disillusionment and disappointment. Not that there are no success stories with digital analytics. There certainly are. But they are comparatively rare. Much more common are legions of valiant but frustrated marketers continuing to struggle with the basics:

Is data collection accurate? Once we learn what the data tells us about our business, are we in a position to do something about it? What happens when our agency tells us they’ve taken care of measurement and, behold, the campaigns are “all good” (or at least not a total waste)? What does change really look like, and can we make it happen in time to matter? How do we do that without automation? And where are the successful predictive models that drive automated responses?

The unanswered questions don’t stop there, but for the sake of brevity we shall.

No one suggests that organizations go without analytics. And many businesses do get to a place where they are comfortable measuring with accuracy and understanding. Many fewer end up being able to fix any but the most egregious “disconnects” between themselves and their customers. The vast majority settle for knowing what happened, with a moderately strong determination to do something about it “in the next release.”

How Digital Can Deliver for Marketers
Many of the most dire threats to success in digital marketing can be overcome by adhering to a process. The process is not very mysterious, and, in fact, can, with some alteration, be applied to almost any endeavor requiring rigor and results.

Following these will go a long way to avoiding disappointment and marketing paralysis, but often it proves devilish hard to get through the process.

Saved by Automation?
The toughest parts of the above process are numbers three and four.

It’s easy enough to figure out your basic metrics and get the data collected properly as long as you have a team of analytics experts. We’re pretty much overrun these days with analysts, but often it’s tough to turn what they say into recommendations. Then, the most difficult part is getting changes made. Figuring out what changes to make, and how to get them made, typically slows the process nearly to a halt.

Automation will be key in changing this from a roadblock to a starting block.

With Tealium’s AudienceStream, you can build in rules and thresholds that send out directives to content delivery systems that let you know it’s time to contact the customer with an offer (for instance). The key to its success is its timeliness and the certainty of its execution. It becomes automatic.

Conductrics deploys Artificial Intelligence to create a system of learning and action based on data. For marketers, this means that Conductrics will facilitate the creation of an “agent” that seeks out challenges and then tackles them (for example, it looks for meaningful patterns and then can direct content to be distributed as needed). Conductrics has likened their agent to a Roomba for digital analytics. It learns its environment and then focuses on doing one task very, very well automatically.

XplusOne [x + 1] markets a product called Origin. According to the firm, “Origin harnesses data to drive real-time, one-to-one interactions across all your digital channels, so every prospect and customer interaction is more relevant.” They also deploy a Data Management Platform that controls numerous customer touchpoints automatically.

These products help conquer the challenges of what many today call omni-channel marketing. They help address how customers can be reached in various “states,” as Rand Schulman has pointed out.

Automation is moving ahead rapidly. It may save analytics by embedding it into an automated process — which probably is where it belongs.

Convergence Analytics 3.0

Written by industry insiders Andrew Edwards and Rand Schulman, Convergence Analytics 3.0 is a Free Guide written for Digital Marketers interested in understanding the most important trends, technologies and practices in analytics today.